Germplasm collections hold diverse alleles shaped by environmental conditions at centers of origin. In Oryza sativa, seed traits impact productivity, consumer preferences, and ecological adaptation, yet their genetic basis—especially in relation to environmental variation—remains unclear. This study seeks to answer the following key questions:
Traditional seed phenotyping is labor-intensive and limited, making high-throughput alternatives essential. This study leverages PlantCV, a computer vision tool, for automated and detailed seed trait analysis, integrating it with GWAS and haplotype-environment association analysis—a largely unexplored combination. This approach enhances trait resolution, uncovering novel genetic candidates missed by conventional methods. By linking genetic variants, seed morphology, and environmental adaptation, this study aims to advance climate-smart breeding, optimizing rice varieties for resilience, productivity, and market preferences.
Fig 1. Oryza spp. Diversity
We generated twelve seed phenotypes with our phenotyping setup, including length, width, and area (Table 1). However, we classified differences observed between subpopulations for the following seed traits as novel differences:
PC1 explained a larger proportion of the variance (51.7%), revealing an inverse relationship between Solidity, Seed Weight, and other traits. PC2 accounted for 27.26% of the variation (Fig 2). Significant subpopulation differences were observed for the Solidity and Minor Axis seed phenotypes (Fig. 3). Specifically, we found a significant difference between the Aus and Indica subpopulations for Solidity, and between the Aus and Aro subpopulations for the Minor Axis.
Fig 2. Principal Component Analysis of Seed Trait Variation